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        Note that additional data was saved in multiqc_data when this report was generated.


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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.24.1

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2024-09-15, 23:46 -03 based on data in: /home/jaldridge/2024/Inach_krill/processing/work/e4/ab8fa27873295105044d8463732b4a/reports

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        General Statistics

        Showing 13/13 rows and 6/15 columns.
        Sample NameReadsRead N50% Dups% GCMedian Read LengthK Seqs
        barcode01
        0.4K
        1431bp
        90.6%
        51%
        1249bp
        430.1K
        barcode02
        0.0K
        1604bp
        0.0%
        52%
        1604bp
        0.0K
        barcode06
        0.3K
        1448bp
        88.5%
        50%
        1249bp
        259.6K
        barcode08
        0.2K
        1436bp
        90.4%
        51%
        1249bp
        202.9K
        barcode09
        0.0K
        2139bp
        0.0%
        50%
        2139bp
        0.0K
        barcode10
        0.2K
        1460bp
        87.7%
        52%
        1499bp
        202.8K
        barcode11
        0.1K
        1411bp
        87.2%
        50%
        1499bp
        121.2K
        barcode12
        0.2K
        1413bp
        88.9%
        50%
        4999bp
        249.6K
        barcode13
        0.5K
        1433bp
        90.7%
        51%
        999bp
        486.2K
        barcode23
        0.0K
        368bp
        0.0%
        54%
        368bp
        0.0K
        reads_nodemux_fail
        0.2K
        3526bp
        38.2%
        47%
        24999bp
        220.0K
        reads_nodemux_pass
        3.4K
        3104bp
        81.6%
        50%
        4999bp
        3395.5K
        unclassified
        1.4K
        3502bp
        68.3%
        50%
        2499bp
        1443.2K

        nanoq

        Reports read quality and length from nanopore sequencing data.URL: https://github.com/nerdna/nanoqDOI: 10.21105/joss.02991

        Nanoq Summary

        Statistics from Nanoq reports

        Showing 13/13 rows and 9/9 columns.
        Sample NameReadsBasesRead N50Longest ReadShortest ReadMean LengthMedian LengthMean QualMedian Qual
        barcode01
        430.1K
        516.8Mb
        1431bp
        12820bp
        1bp
        1201.0bp
        1411
        22.8
        23.0
        barcode02
        0.0K
        0.0Mb
        1604bp
        1604bp
        1604bp
        1604.0bp
        1604
        15.3
        15.3
        barcode06
        259.6K
        362.8Mb
        1448bp
        14944bp
        1bp
        1397.0bp
        1437
        22.0
        22.2
        barcode08
        202.9K
        270.6Mb
        1436bp
        11184bp
        1bp
        1333.0bp
        1435
        23.0
        23.4
        barcode09
        0.0K
        0.0Mb
        2139bp
        2139bp
        2139bp
        2139.0bp
        2139
        19.9
        19.9
        barcode10
        202.8K
        291.2Mb
        1460bp
        10138bp
        1bp
        1436.0bp
        1458
        22.4
        22.8
        barcode11
        121.2K
        142.3Mb
        1411bp
        9739bp
        1bp
        1174.0bp
        1406
        22.5
        22.7
        barcode12
        249.6K
        320.9Mb
        1413bp
        371694bp
        1bp
        1285.0bp
        1410
        22.6
        22.9
        barcode13
        486.2K
        549.3Mb
        1433bp
        73547bp
        1bp
        1129.0bp
        1411
        23.0
        23.1
        barcode23
        0.0K
        0.0Mb
        368bp
        368bp
        368bp
        368.0bp
        368
        26.8
        26.8
        reads_nodemux_fail
        220.0K
        546.6Mb
        3526bp
        1149525bp
        1bp
        2484.0bp
        1523
        7.7
        8.0
        reads_nodemux_pass
        3395.5K
        7636.6Mb
        3104bp
        371694bp
        1bp
        2248.0bp
        1459
        22.0
        22.1
        unclassified
        1443.2K
        5182.5Mb
        3502bp
        190928bp
        1bp
        3590.0bp
        3125
        20.8
        21.1

        Read quality

        Read counts categorised by read quality (Phred score).

        Sequencing machines assign each generated read a quality score using the Phred scale. The phred score represents the liklelyhood that a given read contains errors. High quality reads have a high score.

        Created with MultiQC

        Read lengths

        Read counts categorised by read length.

        Created with MultiQC

        FastQC

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceSamplesOccurrences% of all reads
        CGACTTCACCCCAATCGCTGACCCTACCTTAGGCCGCTGCCTCCTTGCGG
        10
        31405
        0.4479%
        GATGAACGCTGGCGGCGTGCCTAACACATGCAAGTCGAGCGATGAAGTTT
        9
        389064
        5.5492%
        CACCCCAATCGCTGACCCTACCTTAGGCCGCTGCCTCCTTGCGGTTAGCT
        9
        365628
        5.2150%
        CACCCCAGTCATGAATCACAAAGTGGTGAGCGACCTCCCGAAGGTTAGTC
        9
        189568
        2.7038%
        AGTGAACGCTGGCGGCGTGCTTAACACATGCAAGTCGAACGAGAACGGAT
        9
        76200
        1.0868%
        CACCCTAGTTACTAATTCCACTGTGGAAGGTAGCTATTTTAGCATCCCCG
        9
        57448
        0.8194%
        AAGGAACGCTGGCAGCGTGCATAACACATGCAAGTCGAACGATGAAGGAG
        8
        30821
        0.4396%
        ATTGAACGCTGGCGGCAGGCTTAACACATGCAAGTCGAGCGGTAACAGAG
        8
        20249
        0.2888%
        GATGAACGCTGGCGGTATGCCTTACACATGCAAGTTGTACGAGAGTTTTT
        7
        359943
        5.1339%
        CACCCCAGTCATCAATTCTACCTTAGGCATCCTCCTCCAAATAAATGGTT
        7
        363270
        5.1813%
        ATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGCGAAAAGCT
        7
        93634
        1.3355%
        ATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGCGAAAAGCC
        7
        70722
        1.0087%
        ATTGAACGCTGGCGGCAGGCTTAACACATGCAAGTCGAGCGCGAAAGTCC
        7
        17812
        0.2541%
        GATGAACGCTGGCGGTATGCCTTACACATGCAAGTTGTACGAGAGTTTTA
        7
        21225
        0.3027%
        CGACTTCACCCCAGTCATCAATTCTACCTTAGGCATCCTCCTCCAAATAA
        7
        22402
        0.3195%
        CACCCCAATCGCTGACCCTACCTTAGGCCGCTGCCTCCTTACGGTTAGCT
        7
        11813
        0.1685%
        GATGAACGCTGGCGGTATGCCTTACATGCAAGTTGTACGAGAGTTTTTAA
        7
        15398
        0.2196%
        CACCCTAGTCACTAATTCTACCTTAGGCGTCCTCCTCCGAATGGTTAGAG
        7
        10900
        0.1555%
        CACCCTAGTCACCAACCCTGCCTTCGGCATCCTCGTCCCAATGGGTTCGA
        7
        27372
        0.3904%
        ATTGAACGCTGGCGGCAGGCTTAACACATGCAAGTCGAACGGAAACAGGA
        6
        84350
        1.2031%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        SoftwareVersion
        Dorado0.7.3+6e6c45c
        FastQC0.12.1
        Samtools1.20
        nanoq0.10.0
        toulligQC2.7.1

        Software Model Versions

        Showing 1/1 rows and 2/2 columns.
        SoftwareModelVersion
        Dorado
        Basecalling
        dna_r10.4.1_e8.2_400bps_sup@v5.0.0